Application-Oriented License Plate Recognition

We split the applications of vehicle license plate recognition (LPR) into three major categories and propose a solution with parameter settings that are adjustable for different applications. The three categories are access control (AC), law enforcement (LE), and road patrol (RP). Each application i...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2013-02, Vol.62 (2), p.552-561
Main Authors: Hsu, Gee-Sern, Chen, Jiun-Chang, Chung, Yu-Zu
Format: Article
Language:English
Subjects:
Access methods and protocols, osi model
Applied sciences
Cameras
Categories
Character recognition
Character segmentation
Communities
Exact sciences and technology
Image edge detection
Information, signal and communications theory
License plate recognition
License plates
Licenses
Lighting
Pattern recognition
plate detection
Recognition
Roads
Segmentation
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Studies
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
vehicle license plate recognition (LPR)
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We split the applications of vehicle license plate recognition (LPR) into three major categories and propose a solution with parameter settings that are adjustable for different applications. The three categories are access control (AC), law enforcement (LE), and road patrol (RP). Each application is characterized by variables of different variation scopes and thus requires different settings on the solution with which to deal. The proposed solution consists of three modules for plate detection, character segmentation, and recognition. Edge clustering is formulated for solving plate detection for the first time. It is also a novel application of the maximally stable extreme region (MSER) detector to character segmentation. A bilayer classifier, which is improved with an additional null class, is experimentally proven to be better than previous methods for character recognition. To assess the performance of the proposed solution, the application-oriented license plate (AOLP) database is composed and made available to the research community. Experiments show that the proposed solution outperforms many previous solutions, and LPR can be better solved by solutions with settings oriented for different applications.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2012.2226218